Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = '/data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
# Note: because of a version problem with matplot lib, the following line of code doesn't work in this workspace.
# pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f92d2367940>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    #!pip install -U matplotlib==2.2.2
    # TODO: Implement Function
    real_input =  tf.placeholder(tf.float32,[None,image_width,image_height,image_channels])
    z_input = tf.placeholder(tf.float32,[None,z_dim])
    learning_rate = tf.placeholder(tf.float32)
    return (real_input,z_input,learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 478, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 281, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 232, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 397, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2728, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2856, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-6-aaf82213d977>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

### Discriminator
Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of "discriminator" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

  File "<ipython-input-7-9de9ae1fce59>", line 2
    Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of "discriminator" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
              ^
SyntaxError: invalid syntax

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    alpha = 0.0001
    
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides = 2, padding='same')
        relul = tf.maximum(alpha * x1,x1)
        
        x2 = tf.layers.conv2d(relul, 128, 5, strides = 2, padding='same')
        bn_x2 = tf.layers.batch_normalization(x2, training = True)
        relul2 = tf.maximum(alpha * bn_x2, bn_x2)
        
        x3 = tf.layers.conv2d(relul2, 256, 5, strides = 2, padding = 'same')
        bn_x3 = tf.layers.batch_normalization(x3, training = True)
        relul3 = tf.maximum(alpha * bn_x3, bn_x3)
        
        flatten = tf.reshape(relul3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flatten,1)
        out = tf.sigmoid(logits)
        
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    alpha = 0.0001
    
    with tf.variable_scope('generator', reuse = not is_train):
        x1 = tf.layers.dense(z, 7 * 7 * 256)
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training = is_train)
        x1 = tf.maximum(alpha * x1, x1)
        print(x1)
 
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides = 2, padding = 'same')
        x2 = tf.layers.batch_normalization(x2, training = is_train)
        x2 = tf.maximum(alpha * x2, x2)
        print(x2)
        
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides = 2, padding = 'same')
        print(logits)
        
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tensor("Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 5), dtype=float32)
Tensor("Maximum_2:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("Maximum_3:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("conv2d_transpose_4/BiasAdd:0", shape=(?, 28, 28, 5), dtype=float32)
Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1
    g_model = generator(input_z,out_channel_dim,is_train=True)
    d_model_real,d_model_real_logits = discriminator(input_real,reuse=False)
    d_model_fake,d_model_fake_logits = discriminator(g_model,reuse=True)
    

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_model_real_logits,
                                                                         labels=tf.ones_like(d_model_real_logits) *(1-smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_model_fake_logits,
                                                                        labels=tf.zeros_like(d_model_fake_logits)))
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_model_fake_logits,
                                                                   labels=tf.ones_like(d_model_fake_logits)))
    return (d_loss,g_loss)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tensor("generator/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 4), dtype=float32)
Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list= d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(g_loss, var_list = g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
      # TODO: Build Model
    
    _,image_width,image_height,image_channels = data_shape
    
    # getting placeholders
    input_real,input_z,lr = model_inputs(image_width,image_height,image_channels,z_dim)
    
    # getting losses
    d_loss,g_loss = model_loss(input_real,input_z,image_channels)
    
    # Getting optimizers
    d_opt,g_opt = model_opt(d_loss,g_loss,lr,beta1)
    
    step = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                step += 1
                batch_images *= 2
                batch_z = np.random.uniform(-1,1,(batch_size,z_dim))
                _ = sess.run(d_opt,feed_dict={input_real:batch_images,
                                             input_z:batch_z,
                                             lr:learning_rate})
                
                _ = sess.run(g_opt,feed_dict={input_real:batch_images,
                                             input_z:batch_z,
                                             lr:learning_rate})
                
                if step == 1:
                    print('initial output')
                    show_generator_output(sess,25,input_z,image_channels,data_image_mode)
                if step % 10 == 0:
                    train_loss_d = sess.run(d_loss,feed_dict={input_real:batch_images,
                                                    input_z:batch_z,
                                                    lr:learning_rate})
                    train_loss_g = sess.run(g_loss,feed_dict={input_real:batch_images,
                                                    input_z:batch_z,
                                                    lr:learning_rate})
                    print('Epochs {},batches {}, dis_loss {} , gen_loss {}'.format(epoch_i+1,step,train_loss_d,train_loss_g))
                if step%100 == 0:
                    show_generator_output(sess,25,input_z,image_channels,data_image_mode)
      #  saver.save(sess, './generator.ckpt')

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 16
z_dim = 100
learning_rate = 0.0008
beta1 = 0.35



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Tensor("generator/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)
initial output
Tensor("generator_1/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_1/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_1/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 10, dis_loss 0.715658962726593 , gen_loss 12.938607215881348
Epochs 1,batches 20, dis_loss 2.867892026901245 , gen_loss 0.10223448276519775
Epochs 1,batches 30, dis_loss 0.923602283000946 , gen_loss 1.7342052459716797
Epochs 1,batches 40, dis_loss 1.6990776062011719 , gen_loss 0.6527928113937378
Epochs 1,batches 50, dis_loss 1.207032322883606 , gen_loss 0.8399996757507324
Epochs 1,batches 60, dis_loss 1.492093801498413 , gen_loss 2.364034652709961
Epochs 1,batches 70, dis_loss 1.7855932712554932 , gen_loss 1.5791988372802734
Epochs 1,batches 80, dis_loss 1.333063006401062 , gen_loss 1.496023416519165
Epochs 1,batches 90, dis_loss 2.3524274826049805 , gen_loss 0.18383291363716125
Epochs 1,batches 100, dis_loss 1.8380823135375977 , gen_loss 0.32019755244255066
Tensor("generator_2/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_2/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_2/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 110, dis_loss 1.5576995611190796 , gen_loss 0.37722450494766235
Epochs 1,batches 120, dis_loss 1.3977822065353394 , gen_loss 0.6704977750778198
Epochs 1,batches 130, dis_loss 1.0809111595153809 , gen_loss 1.4078142642974854
Epochs 1,batches 140, dis_loss 1.2447121143341064 , gen_loss 1.124820351600647
Epochs 1,batches 150, dis_loss 1.2757896184921265 , gen_loss 1.0675283670425415
Epochs 1,batches 160, dis_loss 2.132389545440674 , gen_loss 0.22067034244537354
Epochs 1,batches 170, dis_loss 2.174490451812744 , gen_loss 0.1905418187379837
Epochs 1,batches 180, dis_loss 1.3635672330856323 , gen_loss 0.5943322777748108
Epochs 1,batches 190, dis_loss 2.4179723262786865 , gen_loss 0.16612158715724945
Epochs 1,batches 200, dis_loss 1.471785545349121 , gen_loss 0.4819257855415344
Tensor("generator_3/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_3/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_3/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 210, dis_loss 0.8929200172424316 , gen_loss 1.1950559616088867
Epochs 1,batches 220, dis_loss 2.1039438247680664 , gen_loss 0.2209913432598114
Epochs 1,batches 230, dis_loss 1.3796781301498413 , gen_loss 2.5048723220825195
Epochs 1,batches 240, dis_loss 1.771635890007019 , gen_loss 0.3564247488975525
Epochs 1,batches 250, dis_loss 1.4524343013763428 , gen_loss 0.6547342538833618
Epochs 1,batches 260, dis_loss 1.2152721881866455 , gen_loss 0.6500643491744995
Epochs 1,batches 270, dis_loss 1.8420677185058594 , gen_loss 3.166682004928589
Epochs 1,batches 280, dis_loss 2.0433225631713867 , gen_loss 0.303891122341156
Epochs 1,batches 290, dis_loss 1.4365918636322021 , gen_loss 0.48495787382125854
Epochs 1,batches 300, dis_loss 1.2457363605499268 , gen_loss 1.8983745574951172
Tensor("generator_4/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_4/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_4/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 310, dis_loss 1.180619716644287 , gen_loss 1.2060017585754395
Epochs 1,batches 320, dis_loss 1.8981988430023193 , gen_loss 0.29932811856269836
Epochs 1,batches 330, dis_loss 1.2195109128952026 , gen_loss 0.6749930381774902
Epochs 1,batches 340, dis_loss 1.6542248725891113 , gen_loss 0.3986802399158478
Epochs 1,batches 350, dis_loss 1.6047759056091309 , gen_loss 0.4057244062423706
Epochs 1,batches 360, dis_loss 1.1347644329071045 , gen_loss 1.1043572425842285
Epochs 1,batches 370, dis_loss 1.2280042171478271 , gen_loss 0.7009851932525635
Epochs 1,batches 380, dis_loss 2.2765722274780273 , gen_loss 0.1877625435590744
Epochs 1,batches 390, dis_loss 1.2224879264831543 , gen_loss 2.268688201904297
Epochs 1,batches 400, dis_loss 1.1796092987060547 , gen_loss 0.6589232087135315
Tensor("generator_5/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_5/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_5/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 410, dis_loss 0.8615534901618958 , gen_loss 1.2358394861221313
Epochs 1,batches 420, dis_loss 1.6623811721801758 , gen_loss 0.4374192953109741
Epochs 1,batches 430, dis_loss 2.1180264949798584 , gen_loss 0.22628796100616455
Epochs 1,batches 440, dis_loss 2.01458740234375 , gen_loss 0.26759105920791626
Epochs 1,batches 450, dis_loss 2.805117607116699 , gen_loss 0.10666566342115402
Epochs 1,batches 460, dis_loss 0.7838354110717773 , gen_loss 1.8208681344985962
Epochs 1,batches 470, dis_loss 0.8483829498291016 , gen_loss 1.3895623683929443
Epochs 1,batches 480, dis_loss 1.5829157829284668 , gen_loss 0.44060617685317993
Epochs 1,batches 490, dis_loss 0.9427911043167114 , gen_loss 1.0357646942138672
Epochs 1,batches 500, dis_loss 0.9857954382896423 , gen_loss 2.0816900730133057
Tensor("generator_6/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_6/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_6/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 510, dis_loss 1.1742403507232666 , gen_loss 0.7419898509979248
Epochs 1,batches 520, dis_loss 1.0627832412719727 , gen_loss 1.1904065608978271
Epochs 1,batches 530, dis_loss 1.0725148916244507 , gen_loss 1.426969289779663
Epochs 1,batches 540, dis_loss 1.5589404106140137 , gen_loss 0.4502969980239868
Epochs 1,batches 550, dis_loss 0.8684704303741455 , gen_loss 1.5644015073776245
Epochs 1,batches 560, dis_loss 1.925015926361084 , gen_loss 0.3452933430671692
Epochs 1,batches 570, dis_loss 1.5503325462341309 , gen_loss 0.3971189856529236
Epochs 1,batches 580, dis_loss 1.7620126008987427 , gen_loss 0.3146393597126007
Epochs 1,batches 590, dis_loss 1.0876060724258423 , gen_loss 1.2706940174102783
Epochs 1,batches 600, dis_loss 1.0162312984466553 , gen_loss 1.7595548629760742
Tensor("generator_7/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_7/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_7/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 610, dis_loss 2.133756637573242 , gen_loss 0.24220043420791626
Epochs 1,batches 620, dis_loss 1.240652084350586 , gen_loss 0.6800557374954224
Epochs 1,batches 630, dis_loss 1.9738528728485107 , gen_loss 0.2740103006362915
Epochs 1,batches 640, dis_loss 0.9382627010345459 , gen_loss 0.9960712790489197
Epochs 1,batches 650, dis_loss 1.660007357597351 , gen_loss 0.41538864374160767
Epochs 1,batches 660, dis_loss 1.6312358379364014 , gen_loss 0.3688056766986847
Epochs 1,batches 670, dis_loss 1.928359866142273 , gen_loss 0.3596895933151245
Epochs 1,batches 680, dis_loss 0.8435566425323486 , gen_loss 1.289363145828247
Epochs 1,batches 690, dis_loss 1.0022286176681519 , gen_loss 0.9432116746902466
Epochs 1,batches 700, dis_loss 0.9966564774513245 , gen_loss 1.7691795825958252
Tensor("generator_8/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_8/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_8/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 710, dis_loss 1.0624183416366577 , gen_loss 0.8576620817184448
Epochs 1,batches 720, dis_loss 0.9957979321479797 , gen_loss 1.239198088645935
Epochs 1,batches 730, dis_loss 1.2649644613265991 , gen_loss 0.6797759532928467
Epochs 1,batches 740, dis_loss 1.2662980556488037 , gen_loss 0.5803343057632446
Epochs 1,batches 750, dis_loss 0.9783986210823059 , gen_loss 2.6460418701171875
Epochs 1,batches 760, dis_loss 0.8792588114738464 , gen_loss 1.4392201900482178
Epochs 1,batches 770, dis_loss 1.0380419492721558 , gen_loss 0.8368186950683594
Epochs 1,batches 780, dis_loss 0.7522392868995667 , gen_loss 1.2235679626464844
Epochs 1,batches 790, dis_loss 1.3538792133331299 , gen_loss 0.5850300788879395
Epochs 1,batches 800, dis_loss 1.213471531867981 , gen_loss 2.3362536430358887
Tensor("generator_9/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_9/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_9/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

Epochs 1,batches 810, dis_loss 1.7421269416809082 , gen_loss 0.3726658821105957
Epochs 1,batches 820, dis_loss 0.7831712961196899 , gen_loss 1.9121805429458618
Epochs 1,batches 830, dis_loss 0.9460383653640747 , gen_loss 2.8707387447357178
Epochs 1,batches 840, dis_loss 2.385516405105591 , gen_loss 0.2121741771697998
Epochs 1,batches 850, dis_loss 0.9445293545722961 , gen_loss 0.9871008992195129
Epochs 1,batches 860, dis_loss 1.7202484607696533 , gen_loss 0.38928279280662537
Epochs 1,batches 870, dis_loss 0.724543571472168 , gen_loss 1.2173693180084229
Epochs 1,batches 880, dis_loss 2.0118021965026855 , gen_loss 0.29541850090026855
Epochs 1,batches 890, dis_loss 1.0825656652450562 , gen_loss 0.7368642091751099
Epochs 1,batches 900, dis_loss 0.8364273905754089 , gen_loss 1.275195598602295
Tensor("generator_10/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_10/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_10/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 1), dtype=float32)

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 16
z_dim = 100
learning_rate = 0.0008
beta1 = 0.35


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Tensor("generator/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)
initial output
Tensor("generator_1/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_1/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_1/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 10, dis_loss 3.3578298091888428 , gen_loss 0.24074004590511322
Epochs 1,batches 20, dis_loss 4.5892438888549805 , gen_loss 0.06624917685985565
Epochs 1,batches 30, dis_loss 1.3961089849472046 , gen_loss 1.1240309476852417
Epochs 1,batches 40, dis_loss 2.1695563793182373 , gen_loss 0.2573181986808777
Epochs 1,batches 50, dis_loss 2.417099952697754 , gen_loss 0.18200165033340454
Epochs 1,batches 60, dis_loss 1.2241874933242798 , gen_loss 1.6471613645553589
Epochs 1,batches 70, dis_loss 1.5221447944641113 , gen_loss 0.6228076219558716
Epochs 1,batches 80, dis_loss 3.03088641166687 , gen_loss 0.08748924732208252
Epochs 1,batches 90, dis_loss 2.2409942150115967 , gen_loss 0.1861652284860611
Epochs 1,batches 100, dis_loss 1.5445046424865723 , gen_loss 1.6855524778366089
Tensor("generator_2/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_2/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_2/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 110, dis_loss 1.436966061592102 , gen_loss 0.7527422904968262
Epochs 1,batches 120, dis_loss 1.2387549877166748 , gen_loss 0.7716234922409058
Epochs 1,batches 130, dis_loss 2.7367610931396484 , gen_loss 0.12087387591600418
Epochs 1,batches 140, dis_loss 1.564174771308899 , gen_loss 2.19044828414917
Epochs 1,batches 150, dis_loss 0.7073236107826233 , gen_loss 1.615647792816162
Epochs 1,batches 160, dis_loss 0.4511689841747284 , gen_loss 2.98262882232666
Epochs 1,batches 170, dis_loss 2.0356035232543945 , gen_loss 2.9900379180908203
Epochs 1,batches 180, dis_loss 1.3860353231430054 , gen_loss 0.5956853628158569
Epochs 1,batches 190, dis_loss 2.063725709915161 , gen_loss 3.0730605125427246
Epochs 1,batches 200, dis_loss 1.8183395862579346 , gen_loss 0.31415730714797974
Tensor("generator_3/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_3/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_3/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 210, dis_loss 0.5414102077484131 , gen_loss 3.064821243286133
Epochs 1,batches 220, dis_loss 2.03216552734375 , gen_loss 0.2637869715690613
Epochs 1,batches 230, dis_loss 1.6821072101593018 , gen_loss 0.3865915536880493
Epochs 1,batches 240, dis_loss 1.6495163440704346 , gen_loss 4.595359802246094
Epochs 1,batches 250, dis_loss 1.2397063970565796 , gen_loss 0.5871280431747437
Epochs 1,batches 260, dis_loss 1.6071093082427979 , gen_loss 0.40993762016296387
Epochs 1,batches 270, dis_loss 1.2141577005386353 , gen_loss 0.734883189201355
Epochs 1,batches 280, dis_loss 0.5254190564155579 , gen_loss 2.2979345321655273
Epochs 1,batches 290, dis_loss 3.3196887969970703 , gen_loss 3.649205207824707
Epochs 1,batches 300, dis_loss 1.3916902542114258 , gen_loss 0.7386164665222168
Tensor("generator_4/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_4/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_4/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 310, dis_loss 1.213057041168213 , gen_loss 0.7316059470176697
Epochs 1,batches 320, dis_loss 1.2047302722930908 , gen_loss 0.8193391561508179
Epochs 1,batches 330, dis_loss 1.2881487607955933 , gen_loss 0.8266964554786682
Epochs 1,batches 340, dis_loss 1.1631052494049072 , gen_loss 1.0177581310272217
Epochs 1,batches 350, dis_loss 1.107497215270996 , gen_loss 2.051496982574463
Epochs 1,batches 360, dis_loss 0.5787640810012817 , gen_loss 1.6329195499420166
Epochs 1,batches 370, dis_loss 1.8104324340820312 , gen_loss 0.34331652522087097
Epochs 1,batches 380, dis_loss 0.5708412528038025 , gen_loss 1.6205105781555176
Epochs 1,batches 390, dis_loss 1.69324791431427 , gen_loss 0.526260256767273
Epochs 1,batches 400, dis_loss 0.7506935596466064 , gen_loss 1.296447992324829
Tensor("generator_5/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_5/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_5/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 410, dis_loss 1.4869880676269531 , gen_loss 0.6760345697402954
Epochs 1,batches 420, dis_loss 1.2115792036056519 , gen_loss 0.908260703086853
Epochs 1,batches 430, dis_loss 1.2136197090148926 , gen_loss 1.4962215423583984
Epochs 1,batches 440, dis_loss 0.9555296897888184 , gen_loss 2.500523567199707
Epochs 1,batches 450, dis_loss 1.0996371507644653 , gen_loss 0.7299427390098572
Epochs 1,batches 460, dis_loss 1.4883878231048584 , gen_loss 2.8885297775268555
Epochs 1,batches 470, dis_loss 0.6737067699432373 , gen_loss 1.6175141334533691
Epochs 1,batches 480, dis_loss 1.822683334350586 , gen_loss 0.2933344542980194
Epochs 1,batches 490, dis_loss 2.0148873329162598 , gen_loss 0.21571679413318634
Epochs 1,batches 500, dis_loss 0.7448381185531616 , gen_loss 2.306533098220825
Tensor("generator_6/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_6/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_6/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 510, dis_loss 0.7086101770401001 , gen_loss 2.597937822341919
Epochs 1,batches 520, dis_loss 0.6022049188613892 , gen_loss 1.658468246459961
Epochs 1,batches 530, dis_loss 1.2836685180664062 , gen_loss 0.7485799789428711
Epochs 1,batches 540, dis_loss 1.3560352325439453 , gen_loss 0.5935052633285522
Epochs 1,batches 550, dis_loss 1.2857533693313599 , gen_loss 2.0834827423095703
Epochs 1,batches 560, dis_loss 0.6745747327804565 , gen_loss 1.3427084684371948
Epochs 1,batches 570, dis_loss 1.174566626548767 , gen_loss 1.0102190971374512
Epochs 1,batches 580, dis_loss 1.0733100175857544 , gen_loss 2.0662546157836914
Epochs 1,batches 590, dis_loss 1.4220296144485474 , gen_loss 0.4979480504989624
Epochs 1,batches 600, dis_loss 0.9947226047515869 , gen_loss 0.9658635854721069
Tensor("generator_7/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_7/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_7/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 610, dis_loss 0.9484495520591736 , gen_loss 1.4033267498016357
Epochs 1,batches 620, dis_loss 0.585010290145874 , gen_loss 1.6343994140625
Epochs 1,batches 630, dis_loss 1.138173222541809 , gen_loss 0.7711799144744873
Epochs 1,batches 640, dis_loss 0.7437444925308228 , gen_loss 1.4216806888580322
Epochs 1,batches 650, dis_loss 1.157947063446045 , gen_loss 0.6250374913215637
Epochs 1,batches 660, dis_loss 1.5238277912139893 , gen_loss 0.39433377981185913
Epochs 1,batches 670, dis_loss 1.6037710905075073 , gen_loss 2.0239996910095215
Epochs 1,batches 680, dis_loss 2.488454818725586 , gen_loss 0.13948389887809753
Epochs 1,batches 690, dis_loss 1.3270659446716309 , gen_loss 0.5126253366470337
Epochs 1,batches 700, dis_loss 0.9532985687255859 , gen_loss 2.848351001739502
Tensor("generator_8/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_8/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_8/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 710, dis_loss 1.0292991399765015 , gen_loss 0.749225378036499
Epochs 1,batches 720, dis_loss 0.8903895020484924 , gen_loss 0.9965931177139282
Epochs 1,batches 730, dis_loss 0.5814117193222046 , gen_loss 1.5782718658447266
Epochs 1,batches 740, dis_loss 3.379728317260742 , gen_loss 0.057144008576869965
Epochs 1,batches 750, dis_loss 1.2823190689086914 , gen_loss 0.6071500778198242
Epochs 1,batches 760, dis_loss 1.2960100173950195 , gen_loss 0.6945411562919617
Epochs 1,batches 770, dis_loss 1.084766149520874 , gen_loss 0.6988019347190857
Epochs 1,batches 780, dis_loss 0.929372251033783 , gen_loss 1.2335373163223267
Epochs 1,batches 790, dis_loss 0.5596474409103394 , gen_loss 2.4349141120910645
Epochs 1,batches 800, dis_loss 1.0801942348480225 , gen_loss 0.7844223976135254
Tensor("generator_9/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_9/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_9/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Epochs 1,batches 810, dis_loss 1.0414478778839111 , gen_loss 0.752410888671875
Epochs 1,batches 820, dis_loss 0.8316175937652588 , gen_loss 1.0301334857940674
Epochs 1,batches 830, dis_loss 0.7638636827468872 , gen_loss 1.1238138675689697
Epochs 1,batches 840, dis_loss 0.5064608454704285 , gen_loss 2.9904422760009766
Epochs 1,batches 850, dis_loss 0.9711819887161255 , gen_loss 0.8594396710395813
Epochs 1,batches 860, dis_loss 0.9482022523880005 , gen_loss 0.8788540959358215
Epochs 1,batches 870, dis_loss 0.4586542248725891 , gen_loss 2.4459915161132812
Epochs 1,batches 880, dis_loss 1.250473976135254 , gen_loss 0.7115038633346558
Epochs 1,batches 890, dis_loss 1.424576997756958 , gen_loss 0.47502702474594116
Epochs 1,batches 900, dis_loss 1.0243759155273438 , gen_loss 0.9926460385322571
Tensor("generator_10/Maximum:0", shape=(?, 7, 7, 256), dtype=float32)
Tensor("generator_10/Maximum_1:0", shape=(?, 14, 14, 128), dtype=float32)
Tensor("generator_10/conv2d_transpose_2/BiasAdd:0", shape=(?, 28, 28, 3), dtype=float32)

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.